Self-Assembled Oligo-Urethane Nanoparticles: Their Characterization and Use for the Delivery of Active Biomolecules into Mammalian Cells
Bibliographic record
Abstract
Developing safe and effective strategies to deliver biomolecules such as oligonucleotides and proteins into cells has grown in importance over recent years, with an increasing demand for non-viral methods that enable clinical translation. Here, we investigate uniquely configured oligo-urethane nanoparticles based on synthetic chemistries that minimize the release of pro-inflammatory biomarkers from immune cells, show low cytotoxicity in a broad range of cells, and efficiently deliver oligonucleotides and proteins into mammalian cells. The mechanism of cell uptake for the self-assembled oligo-urethane nanoparticles was shown to be directed by caveolae-dependent endocytosis in murine myoblasts (C 2 C 12 ) cells. Inhibiting caveolae functions with genistein and methyl-β-cyclodextrin limited nanoparticle internalization. The nanoparticles showed a very high delivery efficiency for the genetic material (a 47-base oligonucleotide) (∼80% incorporation into cells) as well as the purified protein (full length firefly luciferase, 67 kDa) into human embryonic kidney (HEK293T) cells. Luciferase enzyme activity in HEK293T cells demonstrated that intact and functional proteins could be delivered and showed a significant extension of activity retention up to 24 h, well beyond the 2 h half-life of the free enzyme. This study introduces a novel self-assembled oligo-urethane nanoparticle delivery platform with very low associated production costs, enabled by their scalable chemistry (the benchwork cost is $ 0.152/mg vs $ 974.6/mg for typical lipid carriers) that has potential to deliver both oligonucleotides and proteins for biomedical purposes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".